Electrochemistry-based Battery Modeling for Prognostics

Matthew Daigle and Chetan Kulkarni
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_13_040.pdf1.03 MBSeptember 12, 2013 - 11:03am

Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

Publication Year: 
2013
Publication Volume: 
4
Publication Control Number: 
040
Page Count: 
13
Submission Keywords: 
model-based prognostics
lithium-ion batteries
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
Submitted by: 
  
 
 
 

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